Bibliographic record
Abstract
To date there is no systematic exploration of the concept of ‘political feasibility’. We believe that feasibility is a central issue for political philosophy, conceptually as well as practically, and that it has been given background status for far too long. Roughly, a state of affairs is feasible if it is one we could actually bring about. But there are many questions to ask about the conditions under which we are justified in thinking that we could bring about a political state of affairs. In this article we bring together several aspects of the concept of feasibility defended in the literature thus far, and build upon them to give an analysis of the notion of political feasibility. We suggest that the notion involves a relation between agents and the pursuit of certain actions and outcomes in certain historical contexts, and that there are two important roles for feasibility to play in political theory, corresponding to two feasibility ‘tests’: one categorical, the other comparative. We show how the tests operate in the assessment of three different levels of a normative political theory: core normative principles, their institutional implementation and the political reforms leading to them. Focusing on the third level, which has received the least attention in the literature, we proceed to explain how feasibility considerations interact with desirability and epistemic considerations in the articulation of normative political judgments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.085 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".